Sr ML Engineer

AMAN; Muscat; On-site; OMR 15,000 - 26,000; Full time

Job summary

AMAN in Muscat, Oman seeks a Machine Learning Engineer to translate business problems into clearly defined ML use cases, build data pipelines and train models, and deploy AI solutions across cloud and on-prem environments.

You will collaborate with product, software and infrastructure teams to prototype, evaluate and operationalise AI capabilities, including LLMs, RAG and document understanding, while emphasising privacy, security and scalable performance.

Qualifications

• Bachelor’s degree in Computer Science, AI, Data Science, Engineering or a related field.
• 3–5 years of relevant professional experience in ML/AI or related roles.
• Hands-on experience developing, deploying or evaluating ML/AI solutions.
• Strong Python and ML framework experience; practical model development.
• Experience with LLMs, embeddings, vector search and RAG is an advantage.
• Experience with APIs, databases, containerisation and deployment pipelines.

Responsibilities

• Translate business requirements into clearly defined ML use cases.
• Prepare, validate and transform datasets for model development.
• Develop data-processing and machine-learning pipelines.
• Build, train, fine-tune and evaluate ML models using appropriate techniques.
• Develop generative AI and LLM applications, including RAG and semantic search.
• Build AI solutions that interact securely with tools and enterprise systems.
• Design AI solutions for enterprise data privacy, access control and compliance.
• Collaborate with product, software and infrastructure teams throughout the lifecycle.

Skills

Python

LLMs

RAG

Embeddings

APIs

Data pipelines

MLOps

NLP

Statistics

Problem solving

Education

Bachelor’s degree in Computer Science, AI, Data Science, Engineering or related field

Tools

PyTorch

TensorFlow

scikit-learn

Docker

Git

SQL

Cloud platforms

Job description

The Machine Learning Engineer will be responsible for the following:

Machine Learning and AI Development

• Translate business requirements and operational problems into clearly defined machine learning and AI use cases.
• Define measurable success criteria, evaluation methods and practical implementation approaches for AI solutions.
• Prepare, validate and transform datasets for model development and evaluation.
• Develop data-processing and machine-learning pipelines.
• Build, train, fine-tune and evaluate machine learning models using appropriate statistical and machine-learning techniques.
• Develop generative AI and large language model applications.
• Build retrieval-augmented generation (RAG), semantic-search and enterprise knowledge solutions.
• Develop intelligent agents that interact securely with tools, APIs, enterprise applications and other systems.
• Develop document-understanding, information-extraction and AI-assisted document-processing solutions.
• Select and benchmark models based on quality, accuracy, response time, infrastructure requirements, privacy, operating cost and maintainability.

AI Evaluation, Deployment and Operations

• Build repeatable evaluation frameworks for model quality, retrieval performance, grounded responses, hallucination risk, tool usage and agent behaviour.
• Support proof-of-concept, proof-of-value, MVP and pilot implementations.
• Rapidly prototype AI capabilities for customer validation and evolve successful prototypes into maintainable production solutions.
• Work with software, infrastructure and DevOps teams to deploy AI solutions across cloud, private-cloud and on-premises environments.
• Monitor deployed models and AI services and support continuous performance improvement.
• Apply appropriate model versioning, experiment tracking, logging and AI-observability practices.
• Investigate model failures, data-quality issues and unexpected system behaviour and recommend corrective actions.
• Support model optimisation, inference-performance improvement and efficient infrastructure utilisation where required.

Security, Integration and Customer Support

• Design AI solutions that operate within enterprise, government, data-sovereignty and restricted-network requirements.
• Apply appropriate controls for data privacy, access management and secure use of enterprise information.
• Integrate AI models and services through secure APIs and enterprise systems.
• Participate in technical discovery sessions, solution workshops, demonstrations and customer meetings where AI expertise is required.
• Support technical feasibility assessments for proposed AI use cases.
• Prepare technical documentation, evaluation reports and solution documentation.
• Clearly communicate assumptions, limitations, technical risks and dependencies to technical and non-technical stakeholders.
• Work closely with product, business, software-development and infrastructure teams throughout the solution lifecycle.

Any other responsibilities assigned by management as required.

Qualifications and Experience

• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics or a related field.
• 3–5 years of relevant professional experience in machine learning, artificial intelligence, data science or a related technical role.
• At least 2 years of hands-on experience developing, deploying or evaluating machine learning, generative AI or LLM-based solutions.
• Practical experience developing machine-learning solutions beyond experimental notebooks.
• Strong practical experience with Python.
• Experience with machine-learning frameworks such as PyTorch, TensorFlow or scikit-learn.
• Hands-on experience with LLMs, embeddings, vector search, RAG and tool-calling or agent workflows.
• Experience integrating models and AI services through APIs.
• Experience working with SQL, Git, Docker and software deployment pipelines.
• Experience deploying AI solutions into production or customer-facing environments is strongly preferred.
• Experience with private model hosting, on-premises AI or restricted enterprise environments is an advantage.
• Experience in Arabic or multilingual NLP, document AI or OCR is an advantage.
• Experience with Model Context Protocol (MCP), agent orchestration frameworks, MLOps or AI observability is an advantage.
• Experience delivering AI solutions for government or regulated organisations is preferred.
• Equivalent demonstrated technical capability and relevant practical experience may be considered in place of the stated number of years.

Required Skills

• Strong Python and machine-learning development skills.
• Strong understanding of statistics, model validation, data leakage, feature engineering and error analysis.
• Strong understanding of generative AI, LLMs, embeddings, RAG and intelligent-agent concepts.
• Ability to design appropriate evaluation methods for AI and machine-learning solutions.
• Strong data-analysis and problem-solving capabilities.
• Good understanding of APIs, databases, containerisation and deployment concepts.
• Ability to analyse model and system failures systematically.
• Good understanding of AI security, privacy and enterprise-data considerations.
• Ability to balance model quality, performance, infrastructure requirements and operating cost.
• Strong technical documentation and communication skills.
• Ability to work effectively with business, product, software-development and infrastructure teams.
• Ability to manage multiple technical activities and priorities.
• Strong written and verbal communication skills in English; Arabic is an advantage.